Dataset Information

InterBatch Benchmarking Experiment for Isobaric Proteomics

ABSTRACT: We developed a unique multi-batch benchmarking dataset. The design is based on a standard two-species dilution model3,18,19 with mouse plasma used to create a background of potentially interfering peptides at 1:1 ratios while yeast cultures are mixed across a range of known ratios. For the purposes of investigating how best to combine isobaric batches the experiment is designed with two main goals. First, we needed multiple batches of data containing a wide range of known changes, with some large enough to test the dynamic range of our instrument, and others small enough to probe our capacity for detecting small perturbations. Second, we need a wide variety of batch compositions to better reflect the full set of patterns that we might observe when studying a random assortment of genetically diverse samples. To this end, yeast proteomes were diluted at eleven different levels of known changes with a maximum dilution of 1/32 by the use of an automated liquid handler. To generate a diversity of batch compositions across the proteome, we cultured yeast in various carbon and nitrogen source combinations known to substantially alter the yeast proteome20. Both media groups and dilution levels were randomly assigned throughout six batches of TMT-labeled samples

INSTRUMENT(S): Orbitrap Eclipse

ORGANISM(S): Saccharomyces Cerevisiae (baker's Yeast) Mus Musculus (mouse)

TISSUE(S): Blood Plasma

SUBMITTER: Jonathon O'Brien

LAB HEAD: Fiona McAllister

PROVIDER: PXD036799 | Pride | 2024-03-18

REPOSITORIES: Pride

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Publications

A data analysis framework for combining multiple batches increases the power of isobaric proteomics experiments.

O'Brien Jonathon J JJ Raj Anil A Gaun Aleksandr A Waite Adam A Li Wenzhou W Hendrickson David G DG Olsson Niclas N McAllister Fiona E FE

Nature methods 20231218 2

We present a framework for the analysis of multiplexed mass spectrometry proteomics data that reduces estimation error when combining multiple isobaric batches. Variations in the number and quality of observations have long complicated the analysis of isobaric proteomics data. Here we show that the power to detect statistical associations is substantially improved by utilizing models that directly account for known sources of variation in the number and quality of observations that occur across ...[more]

PMID: 38110636

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Project description:Time course of batch growth. Reference (channel 1) was a culture grown in MD medium with 2.4 g/L glucose, with 5 slpm air-flow, stirring at 400 rpm and a constant 300C temperature, dilution 0.25 volumes/hour. The experimental (channel 2) time course samples were grown in batch for the indicated time. The "Low-D chemostat vs. High-D chemostat" hybridization's channel 2 sample was grown in a chemostat, with a dilution rate of 0.05 volumes/hour; it is most similar to the time course samples taken between 8 and 9 hours. Groups of assays that are related as part of a time series. FactorCategory: Elapsed Time; name: Elapsed Time; Measurement: time(absolute) 0 h; name: 45181_Elapsed Time; Measurement: time(absolute) 8.5 h; name: 38150_Elapsed Time; Measurement: time(absolute) 8.75 h; name: 38151_Elapsed Time; Measurement: time(absolute) 9 h; name: 38152_Elapsed Time; Measurement: time(absolute) 9.25 h; name: 38153_Elapsed Time; Measurement: time(absolute) 7.25 h; name: 38145_Elapsed Time; Measurement: time(absolute) 9.5 h; name: 38154_Elapsed Time; Measurement: time(absolute) 7.5 h; name: 38146_Elapsed Time; Measurement: time(absolute) 9.75 h; name: 38155_Elapsed Time; Measurement: time(absolute) 7.75 h; name: 38147_Elapsed Time; Measurement: time(absolute) 10 h; name: 38156_Elapsed Time; Measurement: time(absolute) 8 h; name: 38148_Elapsed Time; Measurement: time(absolute) 8.25 h; name: 38149_Elapsed Time FactorCategory: Growth Condition; name: Growth Condition; Growth Condition: chemostat dilution control; name: 45181_Growth Condition; Growth Condition: batch; name: 38150_Growth Condition; Growth Condition: batch; name: 38151_Growth Condition; Growth Condition: batch; name: 38152_Growth Condition; Growth Condition: batch; name: 38153_Growth Condition; Growth Condition: batch; name: 38145_Growth Condition; Growth Condition: batch; name: 38154_Growth Condition; Growth Condition: batch; name: 38146_Growth Condition; Growth Condition: batch; name: 38155_Growth Condition; Growth Condition: batch; name: 38147_Growth Condition; Growth Condition: batch; name: 38156_Growth Condition; Growth Condition: batch; name: 38148_Growth Condition; Growth Condition: batch; name: 38149_Growth Condition Keywords: time_series_design

Dataset Information

InterBatch Benchmarking Experiment for Isobaric Proteomics

Publications

A data analysis framework for combining multiple batches increases the power of isobaric proteomics experiments.

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